Few-shot learning has been a subject of interest for several decades [1]}, [2]}, [3]}, [4]}, [5]}. Within NLP, “few-shot learning" has traditionally focused on quickly learning new classes and domains within the context of a single task (see [6]} for a recent survey).
Recently, there has been a surge of interest in few-shot learning, following the release of GPT3 [7]}.
Here, the few-shot learning paradigm has shifted subtly, and refers to building models capable of tackling a range of standard NLP tasks, albeit using very restricted training sets, usually sub-sampled from the full training set.
A great deal of work has recently been produced in this area, and we provide a necessarily incomplete summary below.
